Despite much promising research in the area of artificial intelligence for medical image diagnosis, there has been no large-scale validation study done in Thailand to confirm the accuracy and utility of such algorithms when applied to local datasets. Here we present a wide-reaching development and testing of a deep learning algorithm for automated thoracic disease detection, utilizing 421,859 local chest radiographs. Our study shows that convolutional neural networks can achieve remarkable performance in detecting 13 common abnormality conditions on chest X-ray, and the incorporation of local images into the training set is key to the model's success. This paper presents a state-of-the-art model for CXR abnormality detection, reaching an average AUROC of 0.91. This model, if integrated to the workflow, can result in up to 55.6% work reduction for medical practitioners in the CXR analysis process. Our work emphasizes the importance of investing in local research of medical diagnosis algorithms to ensure safe and efficient usage within the intended region.
Instance level detection of thoracic diseases or abnormalities are crucial for automatic diagnosis in chest X-ray images. Most existing works on chest X-rays focus on disease classification and weakly supervised localization. In order to push forward the research on disease classification and localization on chest X-rays. We provide a new benchmark called ChestX-Det10, including box-level annotations of 10 categories of disease/abnormality of $\sim$ 3,500 images. The annotations are located at https://github.com/Deepwise-AILab/ChestX-Det10-Dataset.
Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；Georgia Institute of Technology（佐治亚理工学院）
Thoracic disease detection from chest radiographs using deep learning methods has been an active area of research in the last decade. Most previous methods attempt to focus on the diseased organs of the image by identifying spatial regions responsible for significant contributions to the model's prediction. In contrast, expert radiologists first locate the prominent anatomical structures before determining if those regions are anomalous. Therefore, integrating anatomical knowledge within deep learning models could bring substantial improvement in automatic disease classification. Motivated by …
The University of Hong Kong（香港大学）；Xidian University（西安电子科技大学）；Sun Yat-Sen University（中山大学）；DeepWise AI Lab（深睿医疗人工智能实验室）
Identifying and locating diseases in chest X-rays are very challenging, due to the low visual contrast between normal and abnormal regions, and distortions caused by other overlapping tissues. An interesting phenomenon is that there exist many similar structures in the left and right parts of the chest, such as ribs, lung fields and bronchial tubes. This kind of similarities can be used to identify diseases in chest X-rays, according to the experience of broad-certificated radiologists. Aimed at improving the performance of existing detection methods, we propose a deep end-to-end module to exp…
Johns Hopkins University（约翰斯·霍普金斯大学）；Technical University of Munich（慕尼黑工业大学）；Kyung Hee University（庆熙大学）
Convolutional neural networks are showing promise in the automatic diagnosis of thoracic pathologies on chest x-rays. Their black-box nature has sparked many recent works to explain the prediction via input feature attribution methods (aka saliency methods). However, input feature attribution methods merely identify the importance of input regions for the prediction and lack semantic interpretation of model behavior. In this work, we first identify the semantics associated with internal units (feature maps) of the network. We proceed to investigate the following questions; Does a regression mo…
Deepwise AI Lab（深睿医疗人工智能实验室）；School of Electronics Engineering and Computer Science, University of Peking（北京大学电子工程与计算机科学学院）；School of Automation, University of Northwestern Polytechnical（西北工业大学自动化学院）；Department of Computer Science, University of Hong Kong（香港大学计算机科学系）
Instance level detection and segmentation of thoracic diseases or abnormalities are crucial for automatic diagnosis in chest X-ray images. Leveraging on constant structure and disease relations extracted from domain knowledge, we propose a structure-aware relation network (SAR-Net) extending Mask R-CNN. The SAR-Net consists of three relation modules: 1. the anatomical structure relation module encoding spatial relations between diseases and anatomical parts. 2. the contextual relation module aggregating clues based on query-key pair of disease RoI and lung fields. 3. the disease relation modul…
Harbin Institute of Technology（哈尔滨工业大学）；Southern University of Science and Technology（南方科技大学）；CVTE Research（视源研究院）；Cixi Institute of Biomedical Engineering, Chinese Academy of Sciences（中国科学院慈溪生物医学工程研究所）；University of Hong Kong（香港大学）；Guangdong Armed Police Hospital（广东武警医院）
Chest X-rays are the most commonly available and affordable radiological examination for screening thoracic diseases. According to the domain knowledge of screening chest X-rays, the pathological information usually lay on the lung and heart regions. However, it is costly to acquire region-level annotation in practice, and model training mainly relies on image-level class labels in a weakly supervised manner, which is highly challenging for computer-aided chest X-ray screening. To address this issue, some methods have been proposed recently to identify local regions containing pathological inf…
Deep Reinforcement Learning Framework for Thoracic Diseases Classification via Prior Knowledge Guidance
Tianjin University（天津大学）；Tianjin Medical University General Hospital（天津医科大学总医院）；Chest Hospital, Tianjin University（天津大学胸科医院）；Tianjin Medical University（天津医科大学）；Tianjin Institute of Anesthesiology（天津麻醉学研究所）
VinBigData JSC（VinBigData 公司）；VinUniversity（温纳大学）；Phu Tho Department of Health（富寿省卫生厅）；Phu Tho General Hospital（富寿综合医院）
The development of diagnostic models for detecting and diagnosing pediatric diseases in CXR scans is undertaken due to the lack of high-quality physician-annotated datasets. To overcome this challenge, we introduce and release PediCXR, a new pediatric CXR dataset of 9,125 studies retrospectively collected from a major pediatric hospital in Vietnam between 2020 and 2021. Each scan was manually annotated by a pediatric radiologist with more than ten years of experience. The dataset was labeled for the presence of 36 critical findings and 15 diseases. In particular, each abnormal finding was iden…
Radboud University Medical Center（拉德堡德大学医学中心）；Meander Medical Center（米安德医疗中心）；Jewish National Health（犹太国家健康中心）
Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of morbidity and mortality. While COPD diagnosis is based on lung function tests, early stages and progression of different aspects of the disease can be visible and quantitatively assessed on computed tomography (CT) scans. Many studies have been published that quantify imaging biomarkers related to COPD. In this paper we present a convolutional neural network that directly computes visual emphysema scores and predicts the outcome of lung function tests for 195 CT scans from the COPDGene study. Contrary to previous work, the prop…
Deepwise AI Lab（深度AI实验室）；Nanjing University of Science and Technology（南京理工大学）；Xinhua College of Sun Yat-sen University（中山大学新华学院）；Zhejiang University（浙江大学）；Zhejiang Sci-Tech University（浙江理工大学）；Shaanxi Normal University（陕西师范大学）；University Of Electronic Science And Technology Of China（电子科技大学）；Jiangsu University（江苏大学）
The detection of thoracic abnormalities challenge is organized by the Deepwise AI Lab. The challenge is divided into two rounds. In this paper, we present the results of 6 teams which reach the second round. The challenge adopts the ChestX-Det10 dateset proposed by the Deepwise AI Lab. ChestX-Det10 is the first chest X-Ray dataset with instance-level annotations, including 10 categories of disease/abnormality of 3,543 images. The annotations are located at https://github.com/Deepwise-AILab/ChestX-Det10-Dataset. In the challenge, we randomly split all data into 3001 images for training and 542 …
University of Rochester（罗切斯特大学）；Ludwig Maximilian University（路德维希马克西米利安大学）
Chest x-rays are the most common radiology studies for diagnosing lung and heart disease. Hence, a system for automated pre-reporting of pathologic findings on chest x-rays would greatly enhance radiologists' productivity. To this end, we investigate a deep-learning framework with novel training schemes for classification of different thoracic pathology labels from chest x-rays. We use the currently largest publicly available annotated dataset ChestX-ray14 of 112,120 chest radiographs of 30,805 patients. Each image was annotated with either a 'NoFinding' class, or one or more of 14 thoracic pa…
Chest radiography is one of the most common types of diagnostic radiology exams, which is critical for screening and diagnosis of many different thoracic diseases. Specialized algorithms have been developed to detect several specific pathologies such as lung nodule or lung cancer. However, accurately detecting the presence of multiple diseases from chest X-rays (CXRs) is still a challenging task. This paper presents a supervised multi-label classification framework based on deep convolutional neural networks (CNNs) for predicting the risk of 14 common thoracic diseases. We tackle this problem …
DeepVox and SAVE-CT: a contrast- and dose-independent 3D deep learning approach for thoracic aorta segmentation and aneurysm prediction using computed tomography scans
Albert Einstein Israeli Institute of Education and Research（阿尔伯特·爱因斯坦以色列教育与研究学院）；Health Innovation Techcenter（健康创新技术中心）；Department of Radiology（放射科）；Department of Vascular and Endovascular Surgery（血管与血管外科）
本研究提出 DeepVox 分割模型和 SAVE-CT 分类模型，在独立于对比剂和剂量的 3D CT 扫描上实现全自动胸主动脉瘤筛查，分割和分类测试集表现优异，有望降低死亡率并优化放射科评估队列。
RADLogics；Tel-Aviv University（特拉维夫大学）；Affiliated Taizhou Hospital of Wenzhou Medical University（温州医科大学附属台州医院）；Mount Sinai Hospital（西奈山医院）；University of Maryland School of Medicine（马里兰大学医学院）
Purpose: Develop AI-based automated CT image analysis tools for detection, quantification, and tracking of Coronavirus; demonstrate they can differentiate coronavirus patients from non-patients. Materials and Methods: Multiple international datasets, including from Chinese disease-infected areas were included. We present a system that utilizes robust 2D and 3D deep learning models, modifying and adapting existing AI models and combining them with clinical understanding. We conducted multiple retrospective experiments to analyze the performance of the system in the detection of suspected COVID-…
A Computational Pipeline for Patient-Specific Modeling of Thoracic Aortic Aneurysm: From Medical Image to Finite Element Analysis
Department of Computer Science, University of Miami（迈阿密大学计算机科学系）；Sutra Medical Inc（Sutra医疗公司）；Aortic Institute at Yale-New Haven Hospital, Yale University School of Medicine（耶鲁-纽黑文医院主动脉研究所，耶鲁大学医学院）
Data Science Institute（数据科学研究所）；University of Arizona（亚利桑那大学）
We propose a deep learning based method for classification of commonly occurring pathologies in chest X-ray images. The vast number of publicly available chest X-ray images provides the data necessary for successfully employing deep learning methodologies to reduce the misdiagnosis of thoracic diseases. We applied our method to the classification of two example pathologies, pulmonary nodules and cardiomegaly, and we compared the performance of our method to three existing methods. The results show an improvement in AUC for detection of nodules and cardiomegaly compared to the existing methods.
Google Health, Google（谷歌健康，谷歌）；Apollo Radiology International（阿波罗国际放射医学）；Northwestern Medicine（西北医学）
Chest radiography (CXR) is the most widely-used thoracic clinical imaging modality and is crucial for guiding the management of cardiothoracic conditions. The detection of specific CXR findings has been the main focus of several artificial intelligence (AI) systems. However, the wide range of possible CXR abnormalities makes it impractical to build specific systems to detect every possible condition. In this work, we developed and evaluated an AI system to classify CXRs as normal or abnormal. For development, we used a de-identified dataset of 248,445 patients from a multi-city hospital networ…
Accurate, automatic and complete extraction of pulmonary airway in medical images plays an important role in analyzing thoracic CT volumes such as lung cancer detection, chronic obstructive pulmonary disease (COPD), and bronchoscopic-assisted surgery navigation. However, this task remains challenges, due to the complex tree-like structure of the airways. In this technical report, we use two-stage fully convolutional networks (FCNs) to automatically segment pulmonary airway in thoracic CT scans from multi-sites. Specifically, we firstly adopt a 3D FCN with U-shape network architecture to segmen…
VinUniversity（文大大学）；VinBigData JSC（Vin大数据公司）；Vingroup Big Data Institute（Vingroup大数据研究院）；Hanoi Medical University Hospital（河内医科大学附属医院）；Hospital 108（108医院）
Recent artificial intelligence (AI) algorithms have achieved radiologist-level performance on various medical classification tasks. However, only a few studies addressed the localization of abnormal findings from CXR scans, which is essential in explaining the image-level classification to radiologists. We introduce in this paper an explainable deep learning system called VinDr-CXR that can classify a CXR scan into multiple thoracic diseases and, at the same time, localize most types of critical findings on the image. VinDr-CXR was trained on 51,485 CXR scans with radiologist-provided bounding…
Computer-aided diagnosis via deep learning relies on large-scale annotated data sets, which can be costly when involving expert knowledge. Semi-supervised learning (SSL) mitigates this challenge by leveraging unlabeled data. One effective SSL approach is to regularize the local smoothness of neural functions via perturbations around single data points. In this work, we argue that regularizing the global smoothness of neural functions by filling the void in between data points can further improve SSL. We present a novel SSL approach that trains the neural network on linear mixing of labeled and…
Weakly Supervised Anomaly Detection for Chest X-Ray Image
School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；College of Information and Engineering, Capital Normal University（首都师范大学信息与工程学院）；Peking University Third Hospital（北京大学第三医院）
The First Affiliated Hospital of USTC（中国科学技术大学附属第一医院）；University of Science and Technology of China（中国科学技术大学）；University of Science and Technology（科技大学）；The first affiliated hospital of Bengbu medical college（蚌埠医学院第一附属医院）；AItour（AItour公司）
Background: Triage of patients is important to control the pandemic of coronavirus disease 2019 (COVID-19), especially during the peak of the pandemic when clinical resources become extremely limited. Purpose: To develop a method that automatically segments and quantifies lung and pneumonia lesions with synthetic chest CT and assess disease severity in COVID-19 patients. Materials and Methods: In this study, we incorporated data augmentation to generate synthetic chest CT images using public available datasets (285 datasets from "Lung Nodule Analysis 2016"). The synthetic images and masks were…
Reliable COVID-19 Detection Using Chest X-ray Images
Faculty of Information Technology and Communication Sciences, Tampere University（坦佩雷大学信息技术与通信科学学院）；Department of Electrical Engineering, Qatar University（卡塔尔大学电气工程学院）
Chest X-ray Foundation Model with Global and Local Representations Integration
Department of Biomedical Engineering and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute（生物医学工程系和生物技术及跨学科研究所以及罗切斯特理工学院）；Department of Radiology, Massachusetts General Hospital, Harvard Medical School（放射学系、麻省总医院、哈佛医学院）
Yandex School of Data Analysis（Yandex数据分析学院）；Tel Aviv University（特拉维夫大学）
Content-based retrieval supports a radiologist decision making process by presenting the doctor the most similar cases from the database containing both historical diagnosis and further disease development history. We present a deep learning system that transforms a 3D image of a pulmonary nodule from a CT scan into a low-dimensional embedding vector. We demonstrate that such a vector representation preserves semantic information about the nodule and offers a viable approach for content-based image retrieval (CBIR). We discuss the theoretical limitations of the available datasets and overcome …
Suppression of thoracic bone shadows on chest X-rays (CXRs) has been indicated to improve the diagnosis of pulmonary disease. Previous approaches can be categorized as unsupervised physical and supervised deep learning models. Nevertheless, with physical models able to preserve morphological details but at the cost of extremely long processing time, existing DL methods face challenges of gathering sufficient/qualitative ground truth (GT) for robust training, thus leading to failure in maintaining clinically acceptable false positive rates. We hereby propose a generalizable yet efficient workfl…
University of Houston（休斯顿大学）；University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）
Radiographic images offer an alternative method for the rapid screening and monitoring of Coronavirus Disease 2019 (COVID-19) patients. This approach is limited by the shortage of radiology experts who can provide a timely interpretation of these images. Motivated by this challenge, our paper proposes a novel learning architecture, called Detail-Oriented Capsule Networks (DECAPS), for the automatic diagnosis of COVID-19 from Computed Tomography (CT) scans. Our network combines the strength of Capsule Networks with several architecture improvements meant to boost classification accuracies. Firs…
The Coronavirus disease 2019 (COVID-19) has rapidly spread all over the world since its first report in December 2019 and thoracic computed tomography (CT) has become one of the main tools for its diagnosis. In recent years, deep learning-based approaches have shown impressive performance in myriad image recognition tasks. However, they usually require a large number of annotated data for training. Inspired by Ground Glass Opacity (GGO), a common finding in COIVD-19 patient's CT scans, we proposed in this paper a novel self-supervised pretraining method based on pseudo lesions generation and r…
Region of interest detection for efficient aortic segmentation
Vrije Universiteit Brussel (VUB)（自由大学布鲁塞尔（VUB））；Department of Electronics and Informatics (ETRO)（电子与信息学院（ETRO））；Interuniversity Institute of Bioinformatics in Brussels（布鲁塞尔大学间生物信息研究所）；imec；Université Libre de Bruxelles (ULB)（自由大学布鲁塞尔（ULB））；Machine Learning Group（机器学习组）；Universitair Ziekenhuis Brussel (UZ Brussel)（布鲁塞尔大学医院（UZ Brussel））；Department of Radiology（放射科）
